Toolkit assignment 8

Example of the “bad plot” from the previous assignment

I decided to correct the graph that illustrates election results from the 11 District Electoral Commissions (OKWs) in the Popielów Municipality. The plot I’m going to revise looks like this:

Step 1

First, I needed to load necessary libraries:

library(ggplot2)
library(tidyr)

Step 2

Then, I compiled all the election data I found online (https://popielow.pl/9131/wyniki-wyborow-na-terenie-gminy-popielow.html) into a structured data frame, organizing it by voting district (OKW) and political party to prepare it for visualization.

results <- data.frame(
  Partia = c(
    "KW BEZPARTYJNI SAMORZĄDOWCY",
    "KKW TRZECIA DROGA POLSKA 2050 SZYMONA HOŁOWNI - POLSKIE STRONNICTWO LUDOWE",
    "KW NOWA LEWICA",
    "KW PRAWO I SPRAWIEDLIWOŚĆ",
    "KW KONFEDERACJA WOLNOŚĆ I NIEPODLEGŁOŚĆ",
    "KKW KOALICJA OBYWATELSKA PO .N IPL ZIELONI",
    "W POLSKA JEST JEDNA",
    "KW WYBORCÓW MNIEJSZOŚĆ NIEMIECKA"
  ),
  `OKW 1` = c(4, 23, 17, 52, 7, 50, 0, 13),
  `OKW 2` = c(5, 42, 18, 96, 29, 114, 8, 91),
  `OKW 3` = c(4, 66, 24, 81, 22, 167, 14, 96),
  `OKW 4` = c(9, 74, 18, 58, 26, 133, 5, 81),
  `OKW 5` = c(7, 82, 31, 82, 22, 195, 5, 127),
  `OKW 6` = c(7, 112, 33, 234, 32, 165, 15, 3),
  `OKW 7` = c(8, 43, 14, 135, 33, 76, 2, 3),
  `OKW 8` = c(2, 17, 6, 39, 6, 34, 5, 0),
  `OKW 9` = c(0, 10, 4, 38, 7, 21, 9, 7),
  `OKW 10` = c(2, 33, 11, 82, 16, 59, 0, 0),
  `OKW 11` = c(2, 38, 18, 54, 10, 67, 2, 4)
)

Step 3

Next, using pivot_longer() function from tidyr I reshaped the data frame from wide format into long format, making it easier for ggplot to process.

results_long <- pivot_longer(results, 
                            cols = starts_with("OKW"), 
                            names_to = "OKW", 
                            values_to = "Głosy")

Step 4

In the final step, I made the plots for all the OKWs using the following code:

• First, I used the unique() function to extract a list of all distinct OKWs from the wyniki_long data frame

• Next, I used a for loop to go through all the OKWs to make a plot for each OKW

• The subset() function filters the long-format data frame (wyniki_long) to include only the rows for one specific OKW

• using ggplot() I created a bar chart, where the x-axis represents the political parties (reordered by the number of votes), and the y-axis shows the number of votes

• The coord_flip() function rotates the bars horizontally for better readibility

• I also added appropriate labels and used theme_minimal() for a clean visual style

• Finally, I saved each plot as a .jpg file using ggsave() function, naming the files based on each OKW.

okw_list <- unique(results_long$OKW)

for (okw in okw_list) {
  
  my_data <- subset(results_long, OKW == okw)
  
  my_plot <- ggplot(my_data, aes(x = reorder(Partia, Głosy), y = Głosy, fill = Partia)) +
    geom_bar(stat = "identity") +
    coord_flip() +
    labs(
      title = paste("Wyniki głosowania -", okw),
      x = "Komitet wyborczy",
      y = "Liczba głosów"
    ) +
    theme_minimal()
  
  ggsave(paste0("OKW_plot_", gsub(" ", "_", okw), ".jpg"), plot = my_plot)
}

Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image
Saving 7 x 5 in image

Here’s what the generated plots look like: